mirror of https://github.com/hwchase17/langchain
docs redundant pages (#14774)
[ScaNN](https://python.langchain.com/docs/integrations/providers/scann) and [DynamoDB](https://python.langchain.com/docs/integrations/platforms/aws#aws-dynamodb) pages in `providers` are redundant because we have those references in the Google and AWS platform pages. It is confusing. - I removed unnecessary pages, redirected files to new nams;pull/7287/head
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# AWS DynamoDB
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>[AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html)
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> is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.
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## Installation and Setup
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We have to configur the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html).
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We need to install the `boto3` library.
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```bash
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pip install boto3
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```
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## Memory
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See a [usage example](/docs/integrations/memory/aws_dynamodb).
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```python
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from langchain.memory import DynamoDBChatMessageHistory
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```
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# ScaNN
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>[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
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> (Scalable Nearest Neighbors) is a python package.
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>
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>`ScaNN` is a method for efficient vector similarity search at scale.
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>ScaNN includes search space pruning and quantization for Maximum Inner
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> Product Search and also supports other distance functions such as
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> Euclidean distance. The implementation is optimized for x86 processors
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> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
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> for more details.
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## Installation and Setup
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We need to install `scann` python package.
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```bash
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pip install scann
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```
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## Vector Store
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See a [usage example](/docs/integrations/vectorstores/scann).
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```python
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from langchain.vectorstores import ScaNN
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```
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